Cool to see another model with support for bounding boxes on images. Really wish to see segmentation in a future release; Gemini 3.5 Flash-Lite is mandatory in my use case as it's the best model for semantic image segmentation in my experience.
Using it as part of an agentic CAD workflow, where the model needs to be able to selectively segment from parts of a user's image as it works on a project. Using OpenSCAD so being a general purpose model is very useful to me.
I like that they're being up front about this. Benchmarks are a metric that can be gamed. They give you an approximate estimate of where things land, but you really need to test for your use case.
I used the model and it was good enough for email classification.
I was wondering if I'm supposed to be doing multiple runs for topic classification with grouping related topics i.e. if its sport,economy or tech related news and then another run for soccer,basketball or something else
or batching them like the News article topic classification example in a single run.
Interesting way to release this, I'm not sure how to say this, but this has a real "enterprise"/old school taste to it verses how all the other models/AI companies are releasing things.
Well, they do need to try to compete with SubQ’s longer context and image processing that has been coming out ahead in benchmarks, plus the thousand self-hosted options that now exist.
I walked away with _mostly_ the opposite impression. They have a specific tone of voice, unlike many of the other companies that exist in this landscape.
I've yet to see any company match the copy writing skills of Valve though.
I found an example online, I cannot find the github again, but it was interesting: an instant dynamic generation of React component, you type what you want to build using natural language, and it directly add one to the page.
If I remember well, in the background, it does the multi-choice question: "What is the component requested ? --> Calendar, Slider, ..."
I use it as a drop in replacement for LLMs I had previously doing filtering and classification in a pipeline. It's loads cheaper and faster with roughly equivalent performance on my own internal tests. The confidence scores (which are also reasonably well calibrated on my data) are a super nice bonus.
it's actually more usefull than it looks. it is not much useful on its own but it can play a big role in a complex workflow or custom harness.
i used luna 5.6 as the ranker/filter to remove useless items from upstream rag(mixing embedding, regex etc) and it did a good job but took 5-10 seconds each time. now less than 1 second and no observable precision loss. hope it helps
They don't. Generally we already have trillions tokens worth of classifier datasets so there's no reason to create synthetic data that will be worse than the data it was trained on.
So they held back a bunch of features because they knew they had basically no moat, and now they're trickling out a bit more functionality to try to convince people they are better than others and move fast? And using Apple advert slogans to show how cool they are?
I like that they're being up front about this. Benchmarks are a metric that can be gamed. They give you an approximate estimate of where things land, but you really need to test for your use case.
Very refreshing.
As it's minor change did not expect much, but having a higher context helps for sure. I will own the errors for passing bigger context.
> For the builders AND dreamers
> Enter the danger zone at your own risk.
> Size does matter, apparently
> We’re trying something new out here: we’re going to treat you guys like adults
> Lots of people love to ask what kind of sci-fi we’re releasing next
That said, their docs are great and don't seem to include all the Reddit cringe.
I've yet to see any company match the copy writing skills of Valve though.
If I remember well, in the background, it does the multi-choice question: "What is the component requested ? --> Calendar, Slider, ..."
i used luna 5.6 as the ranker/filter to remove useless items from upstream rag(mixing embedding, regex etc) and it did a good job but took 5-10 seconds each time. now less than 1 second and no observable precision loss. hope it helps
I guess their cheeky "Warning: AI Benchmarks, don't trust them" is their way out of that one.